Model Card for Model ID
Multi-Label Classification Model from the Homework#4 in the Natural Language Processing class of Hanyang University.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Louis MARTYR
- Model type: Multi-Label Classification
- Language(s) (NLP): English
- Finetuned from model [optional]: FacebookAI/roberta-large
Model Sources [optional]
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Uses
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Bias, Risks, and Limitations
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How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
Epoch Training Loss Validation Loss Accuracy F1 Hamming
1 No log 0.072435 0.000000 0.000000 0.013605
2 No log 0.072522 0.000000 0.000000 0.013605
3 0.092900 0.072396 0.000000 0.000000 0.013605
4 0.092900 0.057199 0.000000 0.008461 0.013592
5 0.065500 0.026986 0.064111 0.316517 0.010247
6 0.065500 0.016471 0.773959 0.928884 0.001825
7 0.021900 0.012533 0.884997 0.961644 0.001097
8 0.021900 0.010155 0.917383 0.969257 0.000868
9 0.009300 0.009068 0.916061 0.967037 0.000935
10 0.009300 0.007922 0.923992 0.969573 0.000854
11 0.009300 0.007272 0.924653 0.970616 0.000818
12 0.005900 0.006749 0.929941 0.971468 0.000805
13 0.005900 0.006336 0.931923 0.972127 0.000773
14 0.004300 0.005852 0.931923 0.973525 0.000746
15 0.004300 0.005644 0.938533 0.974937 0.000697
16 0.003500 0.005535 0.931923 0.972501 0.000773
17 0.003500 0.005492 0.936550 0.974324 0.000737
18 0.003000 0.005351 0.937872 0.974378 0.000733
19 0.003000 0.005338 0.937872 0.975060 0.000719
20 0.002700 0.005275 0.940516 0.975551 0.000697 --> Best model
Results
Fine-tuned metrics:
{
'eval_loss': 0.005275276489555836,
'eval_accuracy': 0.9405155320555189,
'eval_f1': 0.97555142119219,
'eval_hamming': 0.0006969079766738156,
'eval_runtime': 7.2009,
'eval_samples_per_second': 210.114,
'eval_steps_per_second': 1.666,
'epoch': 20.0
}
Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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